Intern Data Scientist 2027 – AI & Data Analytics is a role where you will contribute to real client projects involving analytics, generative AI, and agentic AI-enabled transformation. You will build your portfolio by developing reusable code to prepare data, building features, testing models, and creating components for RAG or agentic AI solutions. The work involves applying statistical methods and responsible AI practices to solve complex problems across diverse industries. To succeed, you will utilize a technical stack including Python, SQL, R, notebooks, cloud platforms, APIs, and AI-assisted coding tools like GitHub Copilot or Cursor. You will also leverage frameworks such as pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, LangChain, LangGraph, and LlamaIndex to develop analytical assets while communicating model behaviors and recommendations to both technical and non-technical audiences.
What does a Data Scientist earn?
Median $162025 from 276 postings across 61 companies.
Contribute to client projects involving analytics, generative AI, and agentic AI-enabled transformations.
Develop reusable code to prepare data, build features, and test machine learning models.
Build components for GenAI, RAG, or agentic AI solutions under the guidance of IBM teams.
Apply statistical methods and responsible AI practices to solve complex client challenges.
Use Python, SQL, and cloud platforms to analyze data and create functional analytical assets.
Explain technical results, model behaviors, and recommendations to both technical and non-technical audiences.
Utilize AI-assisted coding tools for testing, debugging, and documentation of software components.
What we're looking for
High School Diploma or GED required.
Bachelor's Degree (preferred).
Currently pursuing a quantitative degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, AI/ML, Cognitive Science, or a related field.
Familiarity with programming and analysis tools such as Python, SQL, R, or similar.
Strong interpersonal skills to collaborate and manage dynamic workloads in an agile environment.
Demonstrated leadership experience and ability to communicate effectively through active listening.
Willingness to travel as needed.
Experience with machine learning frameworks, AI tools, or statistical analysis projects (preferred).